AI Model Selection, Tasks, and Responsible Use Cases
Lesson 2: Data Readiness and Workflow Context
Lesson Objectives
By the end of this lesson, students should be able to:
- List what data a workflow needs.
- Identify missing, stale, private, or biased data risks.
- Decide when to create sample data for training or testing.
Lesson Content
AI quality depends on input quality. For prompting, that means clear context. For classifiers, that means useful examples and labels. For retrieval, that means documents that are current, organized, and relevant. For generated media, that means safe style guidance and review rules.
Data can fail in several ways. It may be missing key cases, too old, collected from the wrong audience, inconsistent, private, or biased. A workflow that looks smart in a demo can fail badly when real data arrives.
Students should build a data readiness note before implementation. It does not need to be fancy. It should answer: What data exists? What is missing? What cannot be used? What examples are needed to test the workflow?
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